Etiological, Clinical and Prognostic Characteristics of Comas in the Multipurpose Intensive Care Unit of The Zinder National Hospital

Article Information

MAGAGI Amadou1*, MAIKASSOUA Mamane2, HASSAN Maman L3 BOUKARI MB4, CHAIBOU MS5, DADDY H6, YAHAYA BN7

1,3 Resuscitation anesthesia department of the Zinder national hospital

2 Faculty of health sciences of the Dan Dicko Dankoulodo University of Maradi

4,5,6. Resuscitation Anesthesia Department of the National Hospital of Niamey

7Faculty of Health Sciences of the André Salifou University of Zinder

*Corresponding Author: MAGAGI Amadou, Faculty of Health Sciences of the André Salifou University of Zinder, department of anesthesia and intensive care at the Zinder national hospital.

Received: 05 December 2023; Accepted: 13 December 2023; Published: 19 January 2024

Citation: MAGAGI Amadou, MAIKASSOUA Mamane, HASSAN Maman L BOUKARI MB, CHAIBOU MS, DADDY H, YAHAYA BN. Etiological, Clinical and Prognostic Characteristics of Comas in the Multipurpose Intensive Care Unit of The Zinder National Hospital. Anesthesia and Critical Care 6 (2024): 01-08.

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Abstract

Coma is a frequent diagnostic and therapeutic emergency. The aim of the study was to enumerate the etiologies of comas and to determine the prognosis of patients. It was a descriptive, cross-sectional study with prospective data collection from 1er May to 31 December 2021, i.e. a period of 8 months in the polyvalent intensive care unit of Zinder National Hospital. The parameters studied were sociodemographic aspects, admission conditions, clinical status on admission, paraclinical data, etiologies of comas, complications, patient outcome. During the study period, 830 patients were admitted, including 112 cases of coma, representing a frequency of 13.5%. The average age was 41.64±22.54 years, with extremes of 3 and 90 years. Males predominated, with a sex ratio of 1.5. Housewives were the most affected (33.9%). High blood pressure was the most frequent antecedent condition. In 66.9% of cases, onset was abrupt, and 47.3% of patients were admitted within 24 hours of coma. The Glasgow score on admission was between 6 and 8 in 47.3% of cases. Traumatic comas accounted for 36.4% of cases, followed by metabolic comas (24.8%) and vascular comas (22.2%). The average length of stay was 5 days. Overall mortality was 48.2%. Poor prognostic factors were: age over 60 (P<0.01), history of hypertension (P=0.02), respiratory distress (P<0.01), tachycardia (P<0.01), coma depth (P<0.01) and hospital stay of less than 3 days (P=0.02). Patients' prognosis depends on early and appropriate management.

Keywords

Etiologies, Prognosis, Comas, HNZ Resuscitation, Introduction

Etiologies articles; Prognosis articles; Comas articles; HNZ Resuscitation articles; Introduction articles

Article Details

1. Introduction

Coma is defined as a state of non-arousable unconsciousness due to dysfunction of the brain's ascending reticular activating system [1].It is a frequent diagnostic and therapeutic emergency requiring rapid and adequate treatment. In France in 2012, the admission rate to intensive care for coma was 48.83% [2]. In Africa, studies have reported an admission rate for comas to intensive care units ranging from 44.4 to 72% [5–8]. In Niamey, Niger in 2016, Yahaya's team recorded an admission rate for serious adult coma of 12.65%.[9]and in 2020, the Boukari MB team reported a rate of 24% among children[10]. The etiology partly determines the prognosis. At the Zinder National Hospital (HNZ), no work has been published on comas. What etiologies and prognosis for these patients? This is how we set ourselves the objective for this work, to list the etiologies and prognosis of comas at the level of our service.

2. Patient and Method

The study took place at the Zinder national hospital in its multipurpose intensive care unit. This was a descriptive, cross-sectional study with prospective data collection which was carried out at the HNZ multipurpose intensive care unit covering a period of 8 months from May 1 to December 31, 2021. All patients admitted to the department multipurpose resuscitation unit of the HNZ. All comatose patients (Glasgow score from 3/15 to 12/15) admitted to the multipurpose intensive care unit of the HNZ during our study period were included in the study. Not included were patients admitted to the intensive care unit with a Glasgow score greater than 12/15, patients operated on under sedation and those comatose after their admission (during hospitalization). The variables studied were age, gender, profession, origin, mode of transport, clinical condition at admission, etiologies, length of stay, outcome. The data collected were recorded and analyzed using Excel 2016 and Epi info version 7 software. Chi2 statistical tests were used to compare qualitative variables, and Kruskal-Wallis and Student to compare proportions and means. . The statistical significance threshold retained was less than 5%.

Informed consent was obtained from the family by explaining the purpose of our study.

The anonymity of the patients was respected: before data analysis, each patient's first and last name was replaced by a number.

3. Results

During the study period, 830 patients were admitted to the intensive care unit, including 112 cases of comatose patients, representing a frequency of 13.5%. The average age was 41.64 ±22.54 years with extremes of 3 and 90 years. The age group of 21 to 40 years was the most represented with 27.6% of cases (n=31).

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Figure 1: Distribution of patients by age group

The male gender represented 59.8% of cases (n=67), i.e. a sex ratio of 1.5.

Table 1: Distribution of patients according to profession

Occupation

Workforce

Percentage

Housewives

38

33.9

Farmer

31

27.6

School

18

16

Without fixed profession

10

8.9

Official

9

8

Trader

6

5.3

Total

112

100

Housewives were the most affected with a rate of 33.9% of cases (n=38).

Table 2: Distribution of patients according to origin

Origin

Workforce

Percentage

Zinder region

102

91.07

Maradi region

4

8

Agadez region

3

3.5

Diffa region

1

2.6

Tahoua region

1

0.8

Others

1

0.8

Total

112

100

The vast majority of patients came from the Zinder region with 91.07% of cases followed by the Maradi region with a rate of 8%.

Table 3: Distribution of patients according to history

Background

Workforce

Percentage

HT

12

10

Diabetes

4

3

Diabetes, hypertension

4

3

Coma, IR

1

0.8

Orchiectomy, herniorrhaphy

1

0.8

hypertension, DALY

1

0.8

Hypertension, heart disease

1

0.8

None

88

78

Total

112

100

The history consisted mainly of arterial hypertension in 12 patients. More than half (78%) of the patients had no history (n=88). The onset of symptoms was sudden in 66.9% of patients (n=75).More than half (49.1%) of the patients (n=55) were admitted 24 hours after the onset of symptoms. The average delay was 25.5 hours. Patients benefited from medical transport in 59.8% of cases.

Table 4: Distribution of patients according to admission constants

Constants Workforce Percentage

Temperature

▪Normal 41 56.9

▪Hyperthermia 31 43.1

FC

▪Normal 31 42.5

▪Tachycardia 42 57.5

P.A.

▪Normal 46 54.1

▪HT 39 45.9

SpO2

▪≥ 95% 48 53.9

▪<95% 41 46.1

Hyperthermia, tachycardia and hypertension were observed in 43.1% respectively; 57.5%; 45.9%. SpO2 was below 95% in 46.1% (n=41) of patients.

In 47.3% (n=53) of patients, the Glasgow score at admission was between 6 and 8, 38.3% between 9 and 12 and 14.2% had a score between 3 and 5.

Hemiplegia was the main focal sign observed in 24.2% of cases (n=25).

Table 5: Distribution of patients according to additional examinations

Additional tests

Workforce

Percentage

Blood sugar

80

71.4

NFS

64

57.1

Creatininemia

60

53.5

Azotemia

59

52.6

Serum electrolytes

52

46.4

GE

36

32.1

Brain scan

40

35.7

TP

2

1.7

ASAT

2

1.7

ALAT

2

1.7

The main tests carried out were blood sugar, CBC, serum creatinine in 71.4% respectively; 57.1%; 53.5%. Brain scanning was only performed in 35.7% of patients.

Table 6: Distribution of patients according to etiologies

Etiologies Numbers Percentage

Cranio-encephalic trauma 41 36.60%

Brain contusion 11 09.8

 ▪Extradural hematoma 02 01.7

 ▪Subdural hematoma 03 02.6

 ▪ND 25 22.3

Metabolic disorders 28 25%

 ▪Renal failure 16 14.2

 ▪Diabetes 11 09.8

 ▪Hepatic encephalopathy 01 00.8

Infections 15 13.39%

 Severe malaria 09 08.0

 ▪Meningoencephalitis 03 02.6

 ▪Brain abscess 01 00.8

 ▪Others 02 01.7

Stroke 25 22.32%

 DALY 14 12.5

 ▪AVCH 08 07.1

 ▪ND 03 02.6

Others 03 02.6%

Total 112,100

Others: intoxication, obstetric, not determined

Cranioencephalic trauma was the most common etiology with a rate of 36.4% (n=41) followed by metabolic coma (25%).

Table 7: Distribution of patients according to decompensations

Decompensations

Workforce

Percentage

Respiratory distress

26

23.6

Convulsive seizures

7

6.3

Septic shock

4

3.6

Respiratory and hyperthermic distress

4

3.6

Septic shockand metabolic disorder

1

0.9

Cardiovascular disorder

3

2.7

Hypoglycemia

1

0.9

Hyperthermic

3

2.7

Respiratory and cardiovascular distress

2

1.8

Hyperthermia and cardiovascular

1

0.9

Seizures and hyperthermia

2

1.8

Seizures and cardiovascular disorder

1

0.9

Bed sore

1

XX0.9

None

54

49

Unspecified

2

1.8

Total

112

100

Respiratory distress was the main decompensation recorded with a rate of 23.6% (n=26).

fortune-biomass-feedstock

Figure 2: Distribution of patients according to prognosis

The death rate was 48.2% (n=54).

Table 8: Influence of age on prognosis

Age

Alive (%) Dead (%) Total (%)

> 60

≤ 60

07(28) 18(72) 25(100)

51(58.6) 36(41.3) 87(100)

Chi2=7.29 p-value<0.01

Age over 60 years was significantly linked to the occurrence of patient death.

Table 9: Influence of history on prognosis

History Living(%) Deceased(%) Total(%)

Yes 08(32) 17(68) 25(100)

No 50(57.4) 37(42.5) 87(100)

Chi2=5.04 p-value=0.02

In 17 cases or 68%, the presence of a history was a factor favoring death.

Table 10: Influence of time to admission to intensive care on prognosis

Admission deadline

Alive(%) Dead(%) Total(%)

≤ 6 hours

> 6 hours

Unspecified

06(66.6) 03(33.3) 09(100)

51(51.5) 48(48.4) 99(100)

01(25) 03(75) 04(100)

Fisher exact= 0.30 p-value= 0.49

Deceased patients were admitted beyond 6 hours in 48.4% of cases.

Table 11: Influence of mode of transport on prognosis

Transport Living(%) Dead(%) Total(%)

Medicalized 29(64.4) 16(35.5) 45(100)

Non-medicalized 30(44.7) 37(55.2) 67(100)

Chi2=0.78 p-value=0.37

Non-medical transport had a mortality rate of 55.2%.

Table12: Influence of altered state of consciousness on prognosis

Glasgow score

 Alive(%) Deceased(%)

Total(%)

3 and 5

6 and 12

3(18.7) 13(81.2)

55(57.2) 41(42.7)

16(100)

96(100)

         

Fisher exact=0.004 p-value<0.01

The deterioration of consciousness significantly influenced the occurrence of death.

4. Discussion

During the study period, 830 patients were recorded, including 112 cases of comas, representing a frequency of 13.5%. Mapoure in Senegal in 2009[5]and Kouamé in Ivory Coast in 2021 [6] had recorded respectively with a rate of 48.99% and 72.11%. The result of this study could be explained by the death of certain patients before admission to the intensive care unit. The average age was 41.64 years with extremes of 3 and 90 years. The age group of 21 to 40 was the most affected with a rate of 27.6%. The youth of the patients was often reported by African authors. Indeed Otiobanda and Mohamed had found a respective average age of 40.5 and 43.9 years [36.37]. However, Adukauskienè in Lithuania in 2020 reported a mean age of 67.6 ± 15.2 years [38]. In Latin America, 69% of patients admitted to the intensive care unit for traumatic coma were aged under 40 [3].The young age found in this study could be explained by the youth of the Nigerien population [39] and the exposure of these young people to public road accidents. We recorded a male predominance with a rate of 59.8%. Which is similar to literature data [40–44]. The explanation lies in the fact that men are more exposed to potential risk factors such as road accidents. But for the Vallin team, to this is added caution, which was much higher among women than men [45]. Housewives were the most affected with a rate of 33.9% in this study. This observation was made by Maimouna Saidou in Mali with 34.2% of housewives [46]. On the other hand, in Benin, Tchaou and Adelin had observed a predominance of independent workers [47.48].This proves that the occurrence of coma is not correlated with occupation. Most patients came from the city of Zinder followed by the department of Magaria and the region of Maradi. This result could be explained for the city of Zinder by the proximity of the intensive care unit; for Magaria it was the absence of an intensive care unit and for Maradi it was the non-functioning of certain services. Arterial hypertension was the main antecedent recorded in 10% of cases. In India in 2020, stroke was the most common cause of non-traumatic coma with a rate of 48.99% [4]. The result of this series was similar to those of Sène Diouf in Senegal in 2009 [49] and Mahamadou Coulibaly in Mali in 2019 [50]. The growing number of hypertensive patients in Niger could be the cause[51–53]. In this series, the beginning was brutal in66.9% of cases.

HoweverBoukari in Niger in 2020 [10] found 71.43% of cases of progressive onset of non-traumatic comas in children aged 7 days to 15 years. In addition, in children, the majority of cases of coma occurred gradually. Infection was the etiology the main etiology of coma in pediatric settings [54–57].Patients were admitted to intensive care 24 hours after the start of symptoms in 49.1% of cases. This result waslower than that ofIbekwe who reported 80% of admission cases after the onset of signs [58]. For Lukman in Nigeria in 2012 59.8% of patients were admitted within 24 hours[59].The result of this study could be explainedby the lack of medical transport and the delay in carrying out the brain scan.Nearly half of the patients had hyperthermia (43.1%), tachycardia (57.5%), hypertension (45.9%) and low SPO2 (46.1%) in this series. In this series, patients with a Glasgow score between 6 and 8 represented 47.3% of cases. Masson had recorded a score below 8 in 69% of patients [61]. The result of this study could be explained by the severity of comas due to the delay in early consultation.

In the majority of patients essentially benefited from: blood sugar, blood count, and serum creatinine. CT was performed in only 35.7% of patients due to the fact that the HNZ did not have a scanner during the study period.The dominant etiologies were cranioencephalic trauma followed by metabolic coma with respectively 36.4% and 24.8% of cases. This same observation was made by Adelin in Benin in 2015. Which also justified the compulsory wearing of helmets in Benin [47]. However, for William's team in the United States in 2002, cranio-encephalic trauma was the third cause of coma with 14% of cases [62]. The result of this study could be explained by the non-compliance with road safety rules favoring public road accidents (AVP), mainly source of the traumatic comas observed in our regions.[63,64]. Respiratory distress was the maindecompensationin this series with a rate of 23.6%. Rate lower than that of Djibril in Togo which reported 51% of cases of respiratory distress[65]. This disorder is due to lesional or metabolic damage to the central structures controlling breathing.[66]. In this series, 48.2% of patients died. However, this result was close to the data in the European literature varying between 43 to 46% [67–71].However, higher mortality was reported by Obiako in Nigeria with a rate of 76% of cases.[72].

The delay in consulting, the delay in treatment and the insufficiency of therapeutic means could explain the result of this study. In this study, older age (over 60 years) was statistically correlated with death (P<0.01). The same observation was reported byOssou-Nguiet PM et al in Brazzaville in 2013[73]and Firsching in Germany in 2017 [74].In fact, elderly people present comorbidities with an increased risk of decompensations further worsening the management [75].Previous history was associated with a significant increase in mortality in this series (P=0.02).Raveloson made the same observation[76]. Delay in seeking consultation was correlated with death (P=0.49). Indeed, the short time to consult would improve the vital prognosis of patients. For some authors this time should not exceed 1 to 3 hours to obtain a good prognosis [77.78].In this series, medical transport improved the vital prognosis of patients. Mendy J in a study carried out in Senegal in 2014 reported a medical transport rate of 5.4% with an overall mortality rate of 34.8% [79]. The result of this study was explained by the absence of an emergency medical aid service (SAMU) in the city and region of Zinder.The most lethal etiologies were stroke, comas of metabolic and infectious origin (P respectively 0.18, 0.51 and 0.49). The study carried out byAdukauskiene in Lithuania in 2020 showed that 74% of deaths were from metabolic coma (P=0.004)[38]. In Niger in 2019, Mansour reported that malaria comas significantly increased the case fatality rate (P=0.0001)[30].

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